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Key generic technology prediction in patent citation using graph neural networks

Abstract

With the rapid advancement of the Fourth Industrial Revolution, international competition in technology and industry is intensifying. However, in the era of big data and large-scale science, making accurate judgments about the key areas of technology and innovative trends has become exceptionally difficult. This paper constructs a patent indicator evaluation system based on the dimensions of key and generic patent citation, integrates graph neural network modeling to predict key common technologies, and confirms the effectiveness of the method using the field of genetic engineering as an example. According to the LDA topic model, the main technical R&D directions in genetic engineering are genetic analysis and detection technologies, the application of microorganisms in industrial production, virology research involving vaccine development and immune responses, high-throughput sequencing and analysis technologies in genomics, targeted drug design and molecular therapeutic strategies, genetically modified crop improvement. The accuracy of predicting key generic technologies related to graph neural networks reaches 97.67%. Based on patent citation theory and the graph neural network models, this paper considers the structural and technical attributes of cited patents, providing theoretical and empirical evidence for technology prediction, and possessing certain theoretical and practical value.